ML Ops Engineer - Lead

Key Responsibilities

• Maintain and enhance existing ML pipelines in On Premise with a focus on infrastructure as code.
• Implement minimal but essential pipeline extensions to support ongoing data science workstreams.
• Convert the Data Science notebooks into production ready deployable components.
• Build ML pipelines for training, inference, monitoring.
• Document infrastructure usage, architecture, and design using tools like Confluence, GitHub Wikis, and system diagrams.
• Act as the internal infrastructure expert, collaborating with data scientists to guide and support ML model deployments.
• Research and implement optimization strategies for ML workflows and infrastructure.
• Work independently and collaboratively with cross-functional teams to support ML product
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